class_docstr: This class implements the task-specific Lingvo ASR model of Qin et al. (2019).
  
  The estimator uses a pre-trained model provided by Qin et al., which is trained using the Lingvo library and the
  LibriSpeech dataset.
  
  | Paper link: http://proceedings.mlr.press/v97/qin19a.html, https://arxiv.org/abs/1902.08295
  
  .. warning:: In order to calculate loss gradients, this estimator requires a user-patched Lingvo module. A patched
               source file for the `lingvo.tasks.asr.decoder` module will be automatically applied. The original
               source file can be found in `<PYTHON_SITE_PACKAGES>/lingvo/tasks/asr/decoder.py` and will be patched as
               outlined in the following commit diff:
               https://github.com/yaq007/lingvo/commit/414e035b2c60372de732c9d67db14d1003be6dd6
  
  The patched `decoder_patched.py` can be found in `ART_DATA_PATH/lingvo/asr`.
  
  Note: Run `python -m site` to obtain a list of possible candidates where to find the `<PYTHON_SITE_PACKAGES` folder.
docstr: Apply padding to a batch of audio samples such that it has shape of (batch_size, max_length).
docstr_tok: ["Apply","padding","to","a","batch","of","audio","samples","such","that","it","has","shape","of","(","batch_size",",","max_length",")","."]
code: def _pad_audio_input(
      x: np.ndarray,
  ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
      """Apply padding to a batch of audio samples such that it has shape of (batch_size, max_length)."""
      max_length = max(map(len, x))
      batch_size = x.shape[0]
  
      # calculate maximum frequency length
      assert (
          max_length >= 480
      ), "Maximum length of audio input must be at least 480."
      frequency_length = [
          ((len(item) // 2 + 1) // 240 * 3) for item in x
      ]
      max_frequency_length = max(frequency_length)
  
      x_padded = np.zeros((batch_size, max_length))
      x_mask = np.zeros((batch_size, max_length), dtype=bool)
      mask_frequency = np.zeros((batch_size, max_frequency_length, 80))
  
      for i, x_i in enumerate(x):
          x_padded[i, : len(x_i)] = x_i
          x_mask[i, : len(x_i)] = 1
          mask_frequency[i, : frequency_length[i], :] = 1
      return x_padded, x_mask, mask_frequency
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  import sys
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
  import numpy
  from art import config
  from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin
  from art.estimators.tensorflow import TensorFlowV2Estimator
  from art.utils import get_file, make_directory
label_desc: Use image node_id 8 for calling the TensorFlowLingvoASR obj's underlying member method code with example usage: obj._pad_audio_input(x) and returns: x_padded,  x_mask,  mask_frequency